Personalized language modeling often seeks to improve the performance of common language tasks, such as generation, using prior knowledge about the human writing the text [1]}.
Personalization can both improve performance as well as make language processing more human-aware [2]}, which can ensure that a more diverse sample of society is included in language models [3]}.
While initially focused on recommendation and generation, human-aware language processing has proven useful to a variety of prediction tasks such as syntactic parsing [4]} and geolocation [5]}.
How to best represent the “human” in such systems remains an open question, and personalized systems often use a writer's demographics [6]} or a writer's social network information [7]} in combination with language to optimize task performance.
One common approach is to convert each writer to a latent representation such as an embedding [8]} and then combine this information with the language context (represented as e.g. word embeddings) in a neural network model, where the writer's representation is learned jointly with the text representations during training [9]}.
We draw inspiration from the contextualized view of personalization from [10]} ([10]}), and we build models to represent the question-askers based on their prior behavior with respect to the specific context of a given question.
